targets_type	autoencoder_type	batch_size
exp_id
3	10_Targets	Over_dim	64
16	10_Targets	normal_dim	32
33	10_Targets	normal_dim_iteration	32
64	10_Targets	Over_dim_tied_iteration	64
65	10_Targets	Over_dim_tied_iteration	32

<string>:6: UserWarning: Warning: converting a masked element to nan.
/home/hicky/anaconda3/envs/tsne/lib/python3.7/site-packages/numpy/ma/core.py:711: UserWarning: Warning: converting a masked element to nan.
  data = np.array(a, copy=False, subok=subok)

<string>:6: UserWarning: Warning: converting a masked element to nan.
/home/hicky/anaconda3/envs/tsne/lib/python3.7/site-packages/numpy/ma/core.py:711: UserWarning: Warning: converting a masked element to nan.
  data = np.array(a, copy=False, subok=subok)

<string>:6: UserWarning: Warning: converting a masked element to nan.
/home/hicky/anaconda3/envs/tsne/lib/python3.7/site-packages/numpy/ma/core.py:711: UserWarning: Warning: converting a masked element to nan.
  data = np.array(a, copy=False, subok=subok)

• Batch size doesn't change the manifold significantly
• Training iteration (train the network with predictions) does influence the manifold, mostly rotation and the cluster formation it becomes slightly worse through the iterations. With Noisy data iteration (train the network with predictions) appears to change nothing.
• Tied weights also seem to make the clustering worse.
• Over_dim_tied_iteration shows in contrast to Over_dim for 10_Targets worsening. This is a combination of the worsening in Over_dim_tied and Over_dim_iteration.
• For Noisy data Over_dim and normal_dim don't present a big difference. Although normal dim produces worse reconstructions
• For Mnist data Over_dim_iteration and normal_dim_iteration don't present a big difference. Although normal dim produces worse reconstructions